Public Domain Mark 1.0https://creativecommons.org/publicdomain/mark/1.0/
License information was derived automatically
This dataset is copied from Eurostat and visualizes the NUTS3 areas as of 2021. It includes information about wether or not the areas are metropolitan areas.
The province of South-Holland also analyzed the population data from Eurostat at Nuts 3 level from 2015 to 2022. We identified areas where the population grew, stayed stable of declined. This information is added to each area.
Created for the Mapping METREX GIS-viewer, hosted by province South-Holland. Link to the viewer: https://experience.arcgis.com/experience/2a24effbf2a1495aa0d20966f4b70886.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Economically active population by sex, age and metropolitan regions
Dataset replaced by: http://data.europa.eu/euodp/data/dataset/mh8eoZP8nkHDGQKrY5Z2g
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This file provides the correspondence table between EUROSTAT NUTS3 classification and the adapted regional classification used by the RISIS-KNOWMAK project. This regional classification fits the structure of knowledge production in Europe and addresses some knowm problems of the NUTS3 classification, such as the treatment of large agglomerations, while remaining fully compatible with the EUROSTAT NUTS regional classification. This compatibility allows combining all KNOWMAK data with regional statistics (at NUTS3 level, 2021 edition) from EUROSTAT.
More precisely, the classification includes EUROSTAT metropolitan regions (based on the aggregation of NUTS3-level regions) and NUTS2 regions for the remaining areas; further, a few additional centers for knowledge production, like Oxford and Leuven, have been singled out at NUTS3 level. The resulting classification is therefore more fine-grained than NUTS2 in the areas with sizeable knowledge production, but at the same time recognizes the central role of metropolitan areas in knowledge production. While remaining compatible with NUTS, the classification allows addressing two well-known shortcomings: a) the fact that some large cities are split between NUTS regions (London) and b) the fact that NUTS3 classification in some countries includes many very small regions, as in the case of Germany
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Employer business demography by NACE Rev. 2 activity and metropolitan regions
Dataset replaced by: http://data.europa.eu/euodp/data/dataset/1ULu4DvwiXz8wDbNOO1w7w
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Community design (CD) applications by metropolitan region (1996-2016)
https://www.verifiedmarketresearch.com/privacy-policy/https://www.verifiedmarketresearch.com/privacy-policy/
Europe Residential Construction Market size was valued at USD 1.08 Trillion in 2024 and is projected to reach USD 1.64 Trillion by 2032, growing at a CAGR of 5.4% from 2026 to 2032.
Europe Residential Construction Market Drivers
Rising Demographic Shifts and Urbanization Trends: The continuous migration to urban centers across Europe is increasing the housing demands, particularly in major metropolitan areas. According to Eurostat’s 2023 data, 75% of the EU population now resides in urban areas, marking a 2.3% increase from the previous year. This urbanization trend has created substantial pressure on residential construction, especially in cities like Berlin, Paris, and Amsterdam, where housing shortages have become increasingly acute.
Growing Sustainable Building Requirements: European nations are witnessing a fundamental shift toward sustainable and energy-efficient housing construction. The European Commission reported in November 2023 that 42% of new residential building permits across the EU now incorporate renewable energy systems. This transformation is driven by stringent environmental regulations, including the EU’s Energy Performance of Buildings Directive, which mandates nearly zero-energy buildings for all new construction.
Escalating Government Housing Initiatives: The European government has intensified its commitment to addressing housing shortages through various support programs and incentives. The German Federal Statistical Office revealed in January 2024 that government spending on residential construction subsidies reached €18.2 billion, representing a 15% increase year-over-year. These initiatives have particularly focused on affordable housing development and first-time homebuyer assistance programs.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Employer business demography by size class and metropolitan region (2008-2019)
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
National annual road freight transport by region of loading, group of goods and metropolitan region
https://www.ons.gov.uk/methodology/geography/licenceshttps://www.ons.gov.uk/methodology/geography/licences
This zip file contains the Standard Area Measurements (SAM) for the 2021 Statistical Areas in England and Wales as at Census Day (21 March 2021). This includes the Output Areas (OA), Lower layer Super Output Areas (LSOA), Middle layer Super Output Areas (MSOA), the Lower-Tier Local Authorities (LTLA) including the Unitary Authorities (E06 and W06), Non-metropolitan Districts (E07), Metropolitan Districts (E08) and London Boroughs (E09), the Upper-Tier Local Authorities (UTLA) including the Unitary Authorities (E06 and W06), Counties (E10), Metropolitan Districts (E08) and London Boroughs (E09), the Regions including the country of Wales, Countries and National. All measurements provided are ‘flat’ as they do not take into account variations in relief e.g. mountains and valleys. Measurements are given in hectares (10,000 square metres) to 2 decimal places and square kilometres to 4 decimal places. Four types of measurements are included: total extent (AREAEHECT), area to mean high water (coastline) (AREACHECT), area of inland water (AREAIHECT) and area to mean high water excluding area of inland water (land area) (AREALHECT). The Eurostat-recommended approach is to use the ‘land area’ measurement to compile population density figures.This V2 is because the user guide name was too long.PLEASE NOTE:There is an extremely small OA with the code E00187556 and measures 400 centimetres squared. This is because all the population and household points are centred around a very small space and to make sure it was in threshold it was manually changed to make it within threshold.Click the Download button to download the files
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Community design (CD) applications per million population by metropolitan region (1996-2016)
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Community design (CD) applications per billion GDP by metropolitan region (1996-2016)
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Employer business demography by NACE Rev. 2activity and metropolitan region (2008-2020)
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Public Domain Mark 1.0https://creativecommons.org/publicdomain/mark/1.0/
License information was derived automatically
This dataset is copied from Eurostat and visualizes the NUTS3 areas as of 2021. It includes information about wether or not the areas are metropolitan areas.
The province of South-Holland also analyzed the population data from Eurostat at Nuts 3 level from 2015 to 2022. We identified areas where the population grew, stayed stable of declined. This information is added to each area.
Created for the Mapping METREX GIS-viewer, hosted by province South-Holland. Link to the viewer: https://experience.arcgis.com/experience/2a24effbf2a1495aa0d20966f4b70886.